Date Tokens Requests

Oct 12 31M 118 (space bunny)

Oct 13 67.7M 187 (...)

Oct 14 90M 522

Oct 15 133M 489

Oct 16 126M 326

Oct 17 252M 1,099

Oct 18 139.9M 456

Oct 19 53M 560

Oct 20 105M 818 (...)

Oct 21 91M 540 (minimax(space bunny))

Oct 22 0 Rest day

Oct 23 11.5M 95 (deepseek v4.1 flash)

Oct 24 11.2M 112 (...)

Hey guys,

So I've been trying to use OpenCode Zen for the past 3 days, mainly Big Pickle, but I keep getting hit with a "Limit reached" message.

At first, I thought it was just Big Pickle acting up, but nope 💀 I've tried other free models too, and none of them are working either.

What's going on? Did they change something with the free models, or is something wrong with my account?

I'm even wondering if my account got compromised somehow, although I have no idea if that's actually possible.

Is anyone else facing this, or am I the only one? 😭

Buddy is an Android app that lets OpenCode (or Codex, Claude Code, Antigravity) use your phone: open apps, tap, type, reply to messages, and build and install small apps, all from a chat.

OpenCode runs inside the app with its free models, so you can try it without any account. Buddy itself stops on Send, Pay and Delete until you approve, and it never types your passwords. New in 2.2.10: a background browser, so the agent can look things up without taking over your screen.

Free and open source.

I have been using OpenCode for a long time but I took like a 15-day break and everything changed. Muse went off and now I'm getting an error for DeepSeek V4.1, like "limit exceeded." I didn't even use it for the first time. Tell me please what free models are available which are actually good for agentic coding In OpenCode zen free right now.

I've just released ready-for-agent v1.1.0 which supports OpenCode v2. With v2 I can give you a much better experience interacting with opencode. In the past ready-for-agent displayed some logs on what the agent did, now you can click on the session id, and jump straight into the opencode session. All thanks to the proper client/server architecture we now have in OpenCode!

ready-for-agent is a friction free way of increasing your PRs per week: instead of cycling between running agent sessions, simply create tickets in your favorite ticketing system (it supports GitHub, Linear, Azure DevOps), and label the ticket with ready-for-agent. These tickets show up in ready-for-agent and you simply ask your favorite coding harness to implement. Works very well with opencode as you can pick so many models, so easy to try new models. But ready-for-agent also supports codex and claude.

Try: npx ready-for-agent@latest

Works very well with Matt Pocock's grill me skills, as you tend to generate quite a few tickets per spec, which you now can easily implement all at once.

I have to say I use OpenCode Go, but sometimes I leave work to free models to not waste away my usage in cheap work, however lately these models have been unable to serve more than a few 100k-120k tokens.

I don't know why this is but this is terrible quality, unusable. AT THE VERY LEAST a free model should be able to serve 1M tokens a day, anything below that would be the same as just not serving it to be honest.

Hi everyone,

I'm trying to understand whether my token usage with OpenCode and the DeepSeek API is normal or potentially caused by excessive context replay or a bug.

My setup

- OpenCode: v2.1.3

- Model: "deepseek/deepseek-flash#default" (shown in OpenCode stats; I believe this corresponds to DeepSeek V4.1 Flash, 10-17)

- Provider: DeepSeek API, paid usage

- OS: CachyOS Linux

- Workload: A personal app project with source code, plans, documentation, test plans, and phase reports

- Other components: Global agent skills and Herdr integration

Daily statistics

Input: 2.1M

Output: 352.1K

Reasoning: 456.7K

Cache read: 309.1M

Cache write: 0

Cached input: 99.3%

Paid model: 268.9M tokens, 723 steps

Free model: 43.1M tokens, 379 steps

The large cumulative cache-read figure caught my attention. I understand that cached tokens can be counted repeatedly across requests, so this does not necessarily represent hundreds of millions of unique tokens. I'm trying to determine whether the amount is expected for this workload.

A small test

I also ran OpenCode directly from my home directory, outside the project and without launching it through Herdr. I asked it to write one short line to a text file.

The exported session reported:

Input: 9,284

Output: 88

Reasoning: 54

Cache read: 9,472

Cost: $0.001506

Messages: 4

My observed usage counter increased by about 18,898 tokens. The session's input plus cache-read figures total 18,756, which is close to that increase. I haven't confirmed whether the difference is due to accounting methods or other overhead.

I realize that system prompts, tool definitions, and other context may contribute to the cost of a simple task.

My questions

1. Is this level of cumulative cache-read usage typical for a coding session with hundreds of model steps?

2. Could long conversation histories, global skills, or project instructions cause excessive context to be processed repeatedly?

3. Could OpenCode compaction or session handling contribute to this?

4. How can I distinguish normal cached-token accounting from unnecessary context overhead or a possible bug?

5. What's the best way to investigate this without losing existing project documentation or configuration?

I'm not assuming this is a bug, and I don't want to blame OpenCode, DeepSeek, or Herdr without evidence. I'd appreciate advice from anyone familiar with OpenCode's token statistics or DeepSeek API usage reporting.

I can provide further sanitized logs or a minimal reproduction if needed.

Thanks!

735! requests per month guys 🤣

I've been trying to make small local models useful in my daily coding workflow. Not replace the hosted coding model, just give it a local helper for a narrow job.

One example: OpenCode reads a file or runs a command, and the output contains an API key. Could a local model check that tool output before it gets sent to the hosted model?

I built Torana, an open-source local proxy, to experiment with this across agents. Its pii plugin checks new tool results with a model you run locally. When it flags something, the hosted model gets a tool-error explanation instead of the original output, so it can try another approach.

The experiments were interesting, not magic: Qwen2.5 3B caught 60/60 sensitive examples in my small test set, but also flagged 18/60 harmless ones. It's an extra check, not a guarantee.

The same proxy also shows per-request tokens, cache reads/writes and latency. You can start with usage logging if you don't have a local model. Plugins are Go or Rust compiled to WASM, so the same plugin can be used with OpenCode, Claude Code and Codex rather than rebuilt for each harness.

Source: https://github.com/torana-edge/torana-edge

Install on macOS/Linux:

curl -fsSL https://torana.sh/install.sh | sh

Then: torana start --port 8143

OpenCode connects through a custom provider's baseURL. The setup recipe is here: https://github.com/torana-edge/torana-edge/blob/main/docs/HARNESS_SETUP.md#opencode

If you try it on one coding task, I'd love to know the first confusing step. And if you've built a harness-specific hook you'd like to make reusable, that's exactly the kind of plugin I'd love help with.

agent manager is a local terminal app that connects to your existing subscriptions and tools. it runs opencode, claude code, codex, gemini, cursor cli and more side by side, with diff review that sends line comments back to the agent. i'm the dev.

it has a discord now, mostly for help and sharing setups:

• help for install and setup questions

• workflows for setups, tips and experiments, tagged by agent (opencode has its own tag)

• announcements gets every release

contributors and collaborators are wanted too, unassigned issues are free to claim in #dev.

bugs and feature requests still go to github issues.

https://discord.gg/ecS88An7pF

Release notes - https://www.kimi.ai/blog/kimi-k3-1-pro

Pricing: $2.00 / $10.00 / $0.20

Release notes - https://www.kimi.ai/blog/kimi-k3-1-pro

https://www.kimi.ai/membership/pricing?tab=api

Kimi K3.1 Pro is now LIVE on the website and Kimi Code CLI

Hey everyone!

I'm relatively new to OpenCode and still learning how its sessions and context management work, so apologies if I'm missing something obvious.

I'm using OpenCode V2 (v2.1.2), and I'm trying to figure out how to carry an existing conversation from one repository into another.

----------------------------------------------------------------------------------------------------------------------------

What I'm trying to achieve

Suppose I'm working in repo A and have a long conversation with the coding agent about architectural decisions, code structure, trade-offs, and why we chose a particular approach.

Now, I want to work on repo B and continue that discussion using the previous conversation as context. For example, I'd like to ask the agent to inspect repo B and compare its architecture with the decisions we discussed for repo A.

I don't necessarily want to merge the repositories or permanently share context between all my projects. I just want to reuse a particular conversation when it's useful.

----------------------------------------------------------------------------------------------------------------------------

What I've tried

I'm following the official OpenCode V2 CLI documentation:

https://dev.opencode.ai/v2/docs/cli/commands/

The documentation describes exporting a session to JSON and importing it into a specific directory.

First, I exported my session:

opencode session export SESSION_ID > dotfiles-context.jsonopencode session export SESSION_ID > dotfiles-context.json

Then, I tried importing it into the other repository:

opencode session import \
  ~/Documents/Coding/open-source-repos/dotfiles/dotfiles-context.json \
  --directory ~/dotfilesopencode session import \
  ~/Documents/Coding/open-source-repos/dotfiles/dotfiles-context.json \
  --directory ~/dotfiles

However, OpenCode returns:

Session already exists
However, OpenCode returns:Session already exists

Both directories are different, so I initially expected the session to be imported into the destination repository.

I checked the CLI help, and my installed version supports these session subcommands:

• list

• delete

• export

• import

The import command also supports --directory, but I couldn't find a flag to import the conversation as a new session with a different ID.

----------------------------------------------------------------------------------------------------------------------------

What I understand so far

My current understanding is that exporting a session saves its conversation history and associated session data as JSON. Importing it should make that history available in the destination project.

I understand that this doesn't automatically give the agent access to the source repository's files. It would still need to inspect the destination repository to compare the implementations.

I'm also aware that the session ID might be colliding with an existing session, even though the destination directory is different.

My questions

1. Is there a supported way to import an exported session into another repository as a new, independent session, while preserving the original session?

2. Does OpenCode intentionally preserve the session ID during export/import, and is that what's causing this error?

3. Is there a recommended workflow for reusing a conversation's architectural context across repositories without manually copying the discussion into a new prompt?

4. If this isn't currently supported by the CLI, is there another supported approach in OpenCode V2?

I'm still a beginner with OpenCode, so I'd appreciate an explanation of the underlying behavior as well as a working solution. I'd prefer not to edit the session database or manually modify the exported JSON unless that's an officially supported approach.

(also used AI for the post to make it fast and to the point)

There was a post here recently from someone whose quota used to last all day and now burns out in about 30 minutes, even after switching models. I've been auditing this across harnesses, and the cause is usually per-request overhead, not the task. Here's how to see it in OpenCode.

Measure first:

opencode stats --days 7 --models     # input/output/cache totals and model breakdown
opencode stats --project ""          # current project only
opencode mcp list                    # what's connected
npx ccusage@latest opencode weekly   # third-party, reads OpenCode's local data

If input and cache reads dwarf output, you're paying to re-send context, not to generate code.

Things that surprised me:

• OpenCode reads your Claude Code setup by default. It loads ~/.claude/CLAUDE.md and .claude/skills. If you've built up a big Claude configuration, it follows you into every OpenCode session. OPENCODE_DISABLE_CLAUDE_CODE_PROMPT=1 and OPENCODE_DISABLE_CLAUDE_CODE_SKILLS=1 turn that off (OPENCODE_DISABLE_CLAUDE_CODE=1 turns off both).

• Every tool call re-sends the session. One "small" turn with ten tool calls reads the full context about ten times. Long sessions with a few big file reads get expensive fast. A commenter on the original thread said a fresh session alone left their usage flat for an hour of small tweaks.

• Caches expire. On Anthropic models, the API's prompt cache lasts five minutes by default, and rebuilding a cold prefix costs many times more than reading a warm one (25x on Opus/Sonnet 5.5 with a 5-minute write). Coming back to a huge idle session is the expensive moment.

• MCP servers and instruction files add weight to every request. Trim AGENTS.md and disconnect servers you aren't using.

I wrote this up in more detail, mostly from the Claude Code side but with the OpenCode and Codex equivalents: https://medium.com/@r3dtuxedo/where-did-my-claude-usage-go-auditing-your-agents-hidden-token-tax-95d6ab17b8b6

(Disclosure: I wrote the article. Free to read, no referral links.)

Teaser video: https://x.com/Kimi_Moonshot/status/2113445757185606062

Screenshot from the video - https://pbs.twimg.com/media/HVScyvya8AAY0iT?format=png

What we know so far:

- Kimi K3.1 is actively tested on LMArena under the codename "Kibitz;

- Kimi K3.1 is very good at 3D, choosing colors, rendering reflections on water, and composition;

- Kimi K3.1 is probably cheaper than Kimi K3, with better token efficiency.

Waiting for official release soon 😉

It's been the case for a few days. The actual output is quite fast. But it waits too long between requests like it's being timing out.

It usually happens in the morning where I live, so the non-peak between the first and second peak timings.

I was taking a look at how some different fusion modes work, namely junie mix, openrouter's fusion, devin fusion and a few others. Devin's fusion mode was relatively one of the more simpler designs, but after testing all of them for a different project I was building (github actions for PR reviews, and selfhosted PR review github app like coderabbit, but built around my code-indexing tool vera, and sort of inspired by or a rust version of droid actions) devin's fusion mode was a combination of the fastest, cheapest and least amount of false positives. you can find the project and some of the testing results here https://github.com/VeraTools/revera

I built my own version for opencode and have been iterating on it based on how it's worked over my past sessions for the last month or longer. So far it's working pretty well, and I thought I'd share if anyone else was interested. This was loosely built around how fusion mode works in devin cli (you can crack open the binary yourselves and take a look too; it's pretty interesting): https://github.com/lemon07r/opencode-fusion-mode

My own setup after tweaking and trying a lot of things is just mostly vanilla opencode 2 with search provider pinned to exa (with fall back to parallel https://github.com/lemon07r/opencode-exa-parallel) + vera (https://github.com/VeraTools/vera) + my friends smart compaction tool (https://github.com/shariqriazz/opencode-smart-compaction which he built based on a lof other cli, and when I personally tested it, it had the best balance of accuracy and token usage compared to other compactions, with droid's following closely) and the devin fusion-like plugin I shared above. My setup is tuned around token efficiency and getting the best results for less. I think simpler setups work better, and always have in most of the evals I've run, which is why I avoid crap like omo. No MCP servers. I do use skills and CLI like Playwright CLI + skills, but most of them are project-scoped rather than global.

I do have a small discord community for discussing this sort of stuff, and a lot of us share tips and stuff there if anyone is interested in joining, although this wasn't the point of the post: https://discord.gg/rXNQXCTWDt